Search

Identifying key classes can help software maintainers quickly understand software systems. The existing key class recognition algorithms consider the weight of class interaction, but the weight mechanism is single or arbitrary. In this paper, the multitype weighting mechanism is considered, and the key classes are accurately identified by using four kinds of interaction. By abstracting the software system into the directed weighted class interaction network, a novel Structure Entropy Weighted LeaderRank of identifying key classes algorithm is proposed. First, considering multiple types and directions of interactions between every pair of classes, the directed weighted class interaction software network (DWCIS-Network) is built. Second, Class Entropy of each class is initialized by the software structural entropy in DWCIS-Network; the Structure Entropy Weighted LeaderRank applies the biased random walk process to iterate Class Entropy. Finally, the iteration is completed to obtain the Final Class Entropy (FCE) of each class as the importance score of each class, top-k classes are obtained, and key classes are identified. For two sets of experiments on Ant and JHotDraw, our approach effectively identifies key classes in class-level software networks for different top-k of classes, and the recall rates of our approach are the highest, 80% and 100%, respectively. From top-15% to top-5%, the precision of our approach is improved by 13.39%, which is the highest in comparison with the precisions of the other two classical approaches. Compared with the best performance of the two classical approaches, the RankingScore of our approach is improved by 16.51% in JHotDraw.

Complex network will suffer from the node or edge failures due to attacks, which may even lead to whole network paralysis. However, the existing methods for measuring network invulnerability are not applicable for the complex network with dual edges. Therefore, in this paper, a dual-direction different-weight complex network model is designed by emphasizing dual edges with different weights. First, the double-level local network is constructed for each node in the model, which is integrated into the weighted entropy. In addition to revealing how the primary-level and the secondary-level of the structure in the model affect invulnerability of one node, a subject-based primary-level node invulnerability measure named DNNP-Entropy and a path-based double-level node invulnerability measure named INNS-Entropy are proposed. Then, on the basis of that, a node double-level local structure weighted entropy (NDLSW-Entropy)-based measure is designed to measure the invulnerability of each node. By using the designed measure, a top invulnerability nodes mining algorithm is proposed to mine nodes with top invulnerability performance. Two groups of experiments are designed, top nodes mined by different measures and network invulnerability entropy under different attack strategies are discussed respectively. Compared with three typical measures on average, the proposed measure increases the precision of top invulnerability nodes about 20%, 30%, 28.3%, 25%, and 38.3% for five reality different size dual-direction different-weight complex networks, respectively.

Brain health quality pre-monitoring has become an urgent need, and this is a system of complex engineering. From the perspective of intelligent decision-making based on big data, the intelligent air index prediction is introduced, the popular classification algorithm is introduced, the hidden information of historical data is mined, and the brain health quality prediction is realized. The brain health quality monitoring system based on the Internet of Things is constructed, and the classification algorithm is used to realize real-time acquisition, intelligent processing of data. In order to improve the data processing speed and enhance the real-time performance of brain health quality prediction, this paper introduces cloud computing technology to accelerate data processing. In order to enable users to understand the air index, anytime and anywhere, it is also designed based on the problem of large historical data of air index and real-time data collection. The Android platform develops an air index forecast client.

Along with the popularity of the optical fiber communication, virtual instrument based online monitoring real-time detecting and automatic diagnosis management system on multi-fiber is proposed in this paper. To manage fiber lines and landmarks, simplified landmark map are presented based on the landmark list. Multi-fiber lines are online monitored, which may have the different or same parameters. When the optical power of any monitored line exceeds the set threshold, the subsystem gives a low power alarm, detecting subsystem will be started automatically to detect alarmed line. After data processing and analysis of detection results, then draws the result curve in the virtual instrument panel. User can locate plot of the curve, and zoom in event curve based on the event list. It utilizes detection results, event list, landmark list and simplified landmarks map synthetically, and presents comprehensive diagnosis conclusion of detection based on the map. To avoid fault, the system predicts fault in future through the analysis of a period of the historical detection result. This system has the advantages of excellent stability, powerful analysis, friendly interface, and convenient operation.

Join queries over wireless sensor data streams need to be processed immediately to keep up with the input streams. Many existing algorithms do not solve the problem in context of both limited CPU and memory resources. In this paper, we propose two CML statistic model based approximate sliding window multi-joins algorithms for the system that both CPU and memory is limited, and a maximum subset of the exact multi-join result is obtained. To shed the load effectively and produce as many join results as possible with the limited amount of resources, both the statistical information of join attributes value of each stream and the relationship between CPU and memory resource is completely considered. The CML statistic model is designed for obtaining and maintaining the statistical information dynamically. With the model, semantic load shedding algorithms are proposed over streaming sliding window multi-join under both limited CPU and memory resource. And a maximizing multi-join output can be generated without delay. Experimental results show that our approach is more efficient than other approach when both the CPU and memory resource are insufficient to keep pace with input streams.

Two novel sampling approaches are proposed to obtain a random sample of exact streaming window join result. Without assuming any model of stream arrivals, the frequency of join attribute values for various basic periods can be obtained by a frequency balanced binary tree histogram (FATH) which is constructed for each stream. The frequency for the future window can be computed by linear regression with the help of the information in the FATH. With the random sample of exact join result produced, a windowed aggregate over the exact join results can be unbiasedly and accurately estimated. Experimental results show that our approach is more efficient than other approach for arbitrary streams.

Join operator has become more and more important in the context of data stream. Most join algorithms over streams to date are based on nested loop joins or hash joins. However, these time expensive algorithms can not suit for CPU-limited case. In this paper, a clustering-based variable-width histogram is designed for obtaining value distribution of tuples in the sliding windows, and some important characteristics of tuples can be retained. Semi-hash tables can be constructed by using the histogram. Our sliding window semi-hash multi-join algorithm can minimize the processing time cost of join and produce an accurate join result much earlier. Experimental results show that our approach is more efficient than other approaches.

In this paper, we introduce a new information flow model for trusted computing systems.Different from traditional system protection models, such as access control matrix model, this model analyze the information flow in the system by tracing the operation sequences , and explains that the security status of a trusted computing system is the security attributes of information flows from input port to output port.With this model, we discuss the information channels in a trusted computing systems, and propose a "normal information flow theorem" which gives a necessary condition of the existence of normal information flow.

We present a novel clustering-based indexing load shedding technique for sliding window joins (CILS). When the details of the distribution of streams are unknown, to obtain the statistics of data, we dynamically maintain a dual indices model (CRA-index and CCRA-index) for each sliding window. The indices are constructed and maintained by clustering technique, and the locality of data is retained. When CPU capacity is insufficient, our index-based load shedding technique is used by utilizing the CRA-index and CCRA-index, a part of tuples are selected and are kept from performing probing, and finally maximum subset join outputs are produced. The indices are space (a fixed amount of memory is used) and time efficient. Experimental results on synthetic and real life data show that our index-based load shedding approach obtains max-subset results effectively, and outperforms the existing load shedding strategies under the same conditions.

We present a novel load shedding technique, called range loading shedding (denoted as RANGE), for sliding window joins when CPU capacity is insufficient in the system and the details of the distribution of streams are unknown. To obtain the statistics of data, we dynamically maintain clustering range histogram (CR-histogram) and average density counter table (ADC-table) for each sliding window. The CR-Histogram is constructed and maintained by clustering technique with a fixed amount of memory. When CPU capacity is insufficient, the RANGE technique is used to select tuples to be processed by utilizing the CR-Histogram and ADC-table, and then produces maximum subset join outputs. Experimental results on synthetic and real life data show that Range load shedding approach obtains max-subset results effectively, and outperforms the existing load shedding strategies.